Exploring Feasibility and Effectiveness of Occasional Whispering in Adults who Stutter: Subjective and Objective Evaluations
Bibliographic record
Abstract
stuttering whispering therapeutic benefits short-term stability subjective evaluation feasibility Stuttering is a challenging condition characterized by disfluencies which prior work has found to be momentarily improved during whispering.The present study explores the clinical potential of whispering by examining whether the benefits of whispering remain stable with short-term use, extend to conversation tasks, and are feasible in daily life.Sixteen adult persons who stutter completed tests assessing the amount of stuttering for normal voiced speech and whispering during both conversation and reading-aloud tasks.Participants then used whispered communication in their daily lives and reported their subjective experiences.After three weeks, effectiveness tests were repeated.Stuttering severity was significantly lower for whispered speech (vs.typical speech) during a conversation task (50% reduction), although this effect was smaller than for the reading-aloud task (85% reduction).This reduction remained present and comparable in magnitude after three weeks of approximately 5 to 10 minutes of daily whispering.Participants subjectively indicated positive experiences with respect to the effects of whispering on fluency and reported that whispering helped reduce stuttering-related anxiety.However, four participants (25%) reported negative voice side effects (e.g., hoarseness, vocal-fold strain), associated with regular whispering.Occasional use of whispering can effectively reduce stuttering and related behaviors during both reading-aloud and conversational speech.This result paves the way for future technological applications that convert whispered speech into natural sounding speech in real-time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".